Calculating expertise confidence based on content and social proximity

ABSTRACT

A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. Application Ser. No.14/573,561, filed on Dec. 17, 2014, the entirety of which isincorporated herein by reference.

BACKGROUND

The present techniques relate to calculating expertise confidence, andmore specifically, to calculating expertise confidence based on contentand social proximity.

SUMMARY

According to an embodiment described herein, a system can include aprocessor. The processor can execute a document-oriented search based ona query in an index of documents to generate a set of document results,each document associated with at least one potential expert. Theprocessor can also analyze the document results to produce a list ofpotential experts. The processor can also further calculate an expertisescore for each potential expert based on a content score and a metadatascore for each potential expert. The processor can further calculate aconfidence score for each potential expert based on adiversity-constrained content score and a diversity-constrained metadatascore for each potential expert. The diversity-constrained content scoreand the diversity-constrained metadata score can be calculated using anevidence diversity score for each potential expert. The processor canalso further send a list of experts with associated confidence scoresthat are above a confidence score threshold to a client device.

According to another embodiment described herein, a method can includeexecuting, via a processor, a document-oriented search based on a queryin an index of documents to generate a set of document results, eachdocument associated with at least one potential expert. The method caninclude analyzing, via the processor, the document results to produce alist of potential experts. The method can also include calculating, viathe processor, an expertise score for each potential expert based on acalculated content score and metadata score for each potential expert.The method can further include calculating, via the processor, anevidence diversity score for each potential expert. The method can alsofurther include calculating, via the processor, a confidence score foreach potential expert based on a diversity-constrained content score anda diversity-constrained metadata score for each potential expert, thediversity-constrained content score and the diversity-constrainedmetadata score to be calculated using the evidence diversity score foreach potential expert. The method can also include displaying a list ofpotential experts with associated confidence scores.

According to another embodiment described herein, a computer programproduct for calculating confidence scores can include computer-readablestorage medium having program code embodied therewith. The computerreadable storage medium is not a transitory signal per se. The programcode can be executable by a processor to cause the processor to execute,via the processor, a document-oriented search based on a query in anindex of documents to generate a set of document results, eachassociated with at least one potential expert. The program code can alsocause the processor to analyze, via the processor, the document resultsto produce a list of potential experts. The program code can also causethe processor to calculate, via the processor, an expertise score foreach potential expert based on a calculated content score and metadatascore for each potential expert and sorting the potential experts by theexpertise score. The program code can also cause the processor toselect, via the processor, a predetermined number of potential expertswith higher expertise scores from the list of potential experts. Theprogram code can also cause the processor to calculate, via theprocessor, an evidence diversity score for each selected expert. Theprogram code can also cause the processor to calculate, via theprocessor, a confidence score for the selected experts based on at leastone of a diversity-constrained content score, a diversity-constrainedmetadata score, and a social score, the diversity-constrained contentscore and the diversity-constrained metadata score to be calculatedusing the evidence diversity score and the social score for each expertto be based on a number of connections to other selected experts. Theprogram code can also cause the processor to filter, via the processor,the selected experts by the confidence score.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of an example computing device that can queryexpertise;

FIG. 2 is a block diagram of an example system for calculatingconfidence scores;

FIG. 3 is a block diagram of an example confidence score calculation;

FIG. 4 is a block diagram of an example social score calculationaccording to embodiments described herein;

FIG. 5 is an example cloud computing environment according toembodiments described herein;

FIG. 6 is example abstraction model layers according to embodimentsdescribed herein;

FIG. 7 is a process flow diagram of an example method that can calculateconfidence scores; and

FIG. 8 is an example tangible, non-transitory computer-readable mediumthat can calculate confidence scores.

DETAILED DESCRIPTION

As a growing number of users are connected by websites and software,identifying a number of users that have expertise in a certain skillsetcan be challenging. For example, some social networking websites mayhave many users that might be experts in one or more areas. Some systemsthat analyze social networking websites and software may rely on usersto provide information indicating areas of expertise, which may includean approval process that validates the user's expertise. Other systemsare based on endorsements of skill sets, wherein uses are encouraged toendorse other people on the social network with certain skills and/orareas of expertise. For example, a level of confidence in the expertiseof a person can be based on how many users have endorsed the person fora given skill or area of expertise. However, such endorsements may notbe thoroughly evaluated or validated and the endorsements may not bebased on any evidence of knowledge in the particular area of expertise.Moreover, skill sets can change over a career. For example, a person mayhave one expertise at one point in time and another a few years later.Additionally, a fan of an area of expertise can produce a lot of contenton an area of expertise but may not be an expert in the area. Also, aperson may have more evidence of expertise than any other person, butthe amount of data available in the area upon which the evidence isbased on may be rather limited. Therefore, simply returning a relativeexpert based upon a small amount of available evidence may not be enoughinformation to make an informed decision in choosing an expert from alist of experts.

According to embodiments of the present disclosure, a confidence scorecan be calculated for potential experts in a network for a plurality ofareas of expertise based on content evidence. A confidence score of aperson's expertise may be used to indicate the relative level ofcertainty of whether a person is actually an expert. A potential expert,as used herein, refers to a person that exhibits practice in an area ofexpertise through for example creation, tagging, commenting or readingof content and may be an expert in the area. In some examples, theconfidence score can be based on both the amount of evidence as well asthe diversity of the evidence. For example, diverse evidence can havemany different types of a particular dimension of evidence. Contentevidence can be diverse if it includes many types of content documentsand/or associations with an expert. Metadata evidence can be diverse ifit includes many types of metadata in connection with an expert.Metadata refers to a list of characteristics describing an expert. Insome examples, the content evidence and metadata evidence can becombined with a score based on social graph analytics for calculating aconfidence score. For example, the social score can be used to determinethe number of other experts in an area of expertise that are connectedto a particular expert. An expertise score based on content evidence andmetadata evidence can be used to rank experts based on the expertisescore for a given expertise. In some examples, a list of ranked expertscan be provided in response to a query for a given expertise along withthe confidence score for that expertise. Thus, embodiments of thepresent disclosure allow potential experts in a network, such as asocial network, to be identified and ranked based on verifiable contentor data. Additionally, the confidence of the ranking system can beimproved by the inclusion of additional dimensions of evidence such associal proximity to other experts in the same area of expertise, amongother dimensions. A dimension is any suitable characteristic that can beused to improve confidence. Thus, the system can effectively distinguishbetween “fans” of areas of expertise that may repost content generatedfrom experts and the experts that actually practice the expertise. Theaddition of other dimensions provides a wider perspective and anefficient approach for calculating a confidence score for each expert.In some examples, the confidence score can be used to filter a list ofexperts based on a threshold level of confidence.

In some scenarios, the techniques described herein may be implemented ina cloud computing environment. As discussed in more detail below inreference to at least FIGS. 1, 5, and 6, a computing device configuredto query expertise may be implemented in a cloud computing environment.It is understood in advance that although this disclosure may include adescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

With reference now to FIG. 1, an example computing device can queryexpertise. The computing device 100 may be for example, a server,desktop computer, laptop computer, tablet computer, or smartphone. Insome examples, computing device 100 may be a cloud computing node.Computing device 100 may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computing device 100 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The computing device 100 may include a processor 102 that is to executestored instructions, a memory device 104 to provide temporary memoryspace for operations of said instructions during operation. Theprocessor can be a single-core processor, multi-core processor,computing cluster, or any number of other configurations. The memory 104can include random access memory (RAM), read only memory, flash memory,or any other suitable memory systems.

The processor 102 may be connected through a system interconnect 106(e.g., PCI®, PCI-Express®, etc.) to an input/output (I/O) deviceinterface 108 adapted to connect the computing device 100 to one or moreI/O devices 110. The I/O devices 110 may include, for example, akeyboard and a pointing device, wherein the pointing device may includea touchpad or a touchscreen, among others. The I/O devices 110 may bebuilt-in components of the computing device 100, or may be devices thatare externally connected to the computing device 100.

The processor 102 may also be linked through the system interconnect 106to a display interface 112 adapted to connect the computing device 100to a display device 114. The display device 114 may include a displayscreen that is a built-in component of the computing device 100. Thedisplay device 114 may also include a computer monitor, television, orprojector, among others, that is externally connected to the computingdevice 100. In addition, a network interface controller (NIC) 116 may beadapted to connect the computing device 100 through the systeminterconnect 106 to the network 118. In some embodiments, the NIC 116can transmit data using any suitable interface or protocol, such as theinternet small computer system interface, among others. The network 118may be a cellular network, a radio network, a wide area network (WAN), alocal area network (LAN), or the Internet, among others. An externalcomputing device 120 may connect to the computing device 100 through thenetwork 118. In some examples, external computing device 120 may be anexternal webserver 120. In some examples, external computing device 120may be a cloud computing node.

The processor 102 may also be linked through the system interconnect 106to a storage device 122 that can include a hard drive, an optical drive,a USB flash drive, an array of drives, or any combinations thereof. Insome examples, the storage device may include a search engine 124, asocial graph module 126, and a confidence module 128. In some examples,the query received by the search engine 124 can include an expertise.The search engine 124 can execute a document-oriented search based on aquery in an index of documents to generate or output a set of documentresults. Each document can be associated with at least one potentialexpert. For example, a document can be a blog post, a social media post,a shared file, and the like. The search engine 124 can analyze thedocument results to produce a list of potential experts. The confidencemodule 128 can calculate an expertise score for each potential expertbased on a content score and a metadata score for each potential expert.In some examples, the confidence module 128 can calculate the metadatascore from tags, skills, and job titles of the potential experts storedin a data repository. In some examples, the confidence module 128 cancalculate the content score based on content document types such aswikis, blogs, forums, files, and the like, the content document types tobe gathered by parsing websites and stored in a data repository. Thecontent score can also be based on associations of a potential expertwith the content documents, such as being a commenter, author, or likerof the content document. In some examples, a combination of a documenttype and association can be given a predetermined amount of contentscore points. In some examples, the confidence module 128 can select apredetermined number of selected experts with expertise scores above athreshold from the list of potential experts and calculate an evidencediversity score for each selected expert. In some examples, theconfidence module 128 can also calculate an evidence diversity score foreach potential expert. The confidence module 128 can also calculate aconfidence score for the selected experts or potential experts based ona diversity-constrained content score and a diversity-constrainedmetadata score. For example, the diversity constrained content score andthe diversity-constrained metadata score can be based on the evidencediversity score. The content score and the metadata score for eachselected expert or potential expert can be constrained by the evidencediversity score to generate the diversity-content score and thediversity-constrained metadata score. In some examples, the confidencescore can also be based on a social score. The social graph module 126can generate a graph of connections between the potential experts. Insome examples, the social graph module 126 can generate a social graphbased on a predetermined number of selected experts. The social graphmodule 126 can calculate a social score for each selected expert orpotential expert using the graph.

In some examples, the confidence scores may be based on preconfiguredthresholds. For example, the thresholds may be used to separate a lowconfidence score, a medium confidence score, and a high confidencescore. The confidence scores can indicate a certainty of expertise foreach selected expert or potential expert. In some embodiments, thesearch engine 124 can then send a list of experts with associatedconfidence scores to a client device. In some examples, the list ofexperts can be filtered to the potential experts or selected expertsthat have confidence scores above a threshold score. In some examples,the list of experts can be sorted by the expertise score of eachpotential expert or selected expert.

It is to be understood that the block diagram of FIG. 1 is not intendedto indicate that the computing device 100 is to include all of thecomponents shown in FIG. 1. Rather, the computing device 100 can includefewer or additional components not illustrated in FIG. 1 (e.g.,additional memory components, embedded controllers, modules, additionalnetwork interfaces, etc.). Furthermore, any of the functionalities ofthe search engine 124, social graph module 126, and confidence engine128 may be partially, or entirely, implemented in hardware and/or in theprocessor 102. For example, the functionality may be implemented with anapplication specific integrated circuit, logic implemented in anembedded controller, or in logic implemented in the processor 102, amongothers. In some embodiments, the functionalities of the search engine124, social graph module 126, and confidence engine 128 can beimplemented with logic, wherein the logic, as referred to herein, caninclude any suitable hardware (e.g., a processor, among others),software (e.g., an application, among others), firmware, or any suitablecombination of hardware, software, and firmware.

FIG. 2 is a block diagram of an example system for calculatingconfidence scores. The example system of FIG. 2 is generally referred toby the reference number 200.

In FIG. 2, the system 200 includes a client device 202. The system 200also includes a search engine 204 with a search engine configuration206, a search index 208, and a social graph 210. For example, the searchengine, 204, search index 208 and social graph 110 can be stored in astorage device, such as storage device 122. The search index 208includes expert profiles 211, content documents 212, and metadata 214.The expert profiles 211 can have associations 216 with one or morecontent documents 212 and metadata 214. The social graph 210 can includea plurality of potential experts 218 or selected experts 218 joined bysocial connections 220. The social graph 210 and the search engine 204are communicatively coupled to the search index 208 via connection 222and connection 224, respectively. The search engine 204 includes aconfiguration file 206. The client device 202 can also send a query 226to the search engine 204 and receive a response including results 228.

As shown in FIG. 2, a query 226 can be sent from the client device 202to the search engine 204. For example, the query 226 can include anexpertise for which one or more potential experts 218 may exist. Basedon the search engine configuration file 206, the search engine 204 canexecute one or more searches on search index 208. In some examples, thesearch engine 204 can execute a document-oriented search of the contentdocuments 212 in the search index 208. For example, the search can bebased on a query 226 for cloud computing expertise, among others. Insome examples, each content document 212 can be associated 216 with atleast one potential expert 218 in the expert profiles 211. For example,at least one potential expert 218 may have created, modified, liked, oraltered the content document 212 in some manner. In some examples, thesearch engine 204 can analyze the document results of the search toproduce a list of potential experts. In some examples, the documentresults can include content documents 212 related to a particularexpertise. In addition, the analysis can return one or more experts thatare associated with the content documents 212. The results 228 of thequery 226, such as the list of expert profiles 211 corresponding topotential experts, can then be ranked via expertise scores as describedin greater detail below.

In some examples, the search engine 204 calculates an expertise scorefor the expert profiles 211 based on metadata 214 associated with theexpert profiles 211. The expertise scores can be used to identify usersthat qualify as experts in an area of expertise specified by the query226. In some embodiments, the metadata can include tags, skills or jobtitles associated with the potential expert profiles 211. In someexamples, the search engine 204 calculates an expertise score based inpart on the diversity of the content documents 212. For example, anexpert associated with three types of documents for an area ofexpertise, such as blogs, wall posts, and articles, can receive a higherconfidence score than an expert associated with reposting content orsharing content. In some examples, the diversity of the documents can beused as a constraint. A constraint, as referred to herein, can includeany suitable characteristic that is used to limit a confidence score.For example, expertise scores may be allowed a higher rating or rankingif the expertise score is based on multiple types of documents orlimited to a lower rating or ranking if the expertise score is based onone or two types of documents. In some examples, other dimensions can beused as constraints. In some examples, strength of the contentassociations can be used as a dimension. For example, having at leasttwo strong associations such as being tagged in a document associatedwith an area of expertise or owning an online community related to anarea of expertise can be used as a dimension for high confidence levels.In some examples, validity according to date can be used as aconstraint. For example, a constraint for higher expertise scores cancorrespond to the number of documents associated with a user from a timeperiod. In some examples, the search engine 204 also calculates theconfidence score using social graph data. For example, the number ofconnections 220 between a given potential expert 218 and other potentialexperts 218 can be used to increase the confidence score for awell-connected potential expert. In some examples, a social graph 210can be constructed for each query 226 of a particular area of expertise.The social graph 210 can then be used in calculating a confidence scorefor each profile 211 that has been selected as a potential expert in thearea of expertise. The detailed calculation of a confidence score foreach potential expert is discussed at greater length with reference toFIG. 3 below.

Still referring to FIG. 2, in some examples, response 228 includes alist of experts for a given expertise with a confidence score thatexceeds a threshold value. In some examples, the confidence score alongwith content evidence such as content documents 212 for the confidencescore can also be provided to client device 202 and displayed.

It is to be understood that the block diagram of FIG. 2 is not intendedto indicate that the system 200 is to include all of the componentsshown in FIG. 2. Rather, the system 200 can include fewer or additionalcomponents not illustrated in FIG. 2 (e.g., additional dimensions, oradditional indexes, etc.). For example, alternatively, or in addition tothe diversity of evidence dimension, strength of the contentassociations and validity according to date can be used, among otherdimensions.

FIG. 3 is a block diagram of an example confidence score calculation.The example system of FIG. 3 is generally referred to by the referencenumber 300.

In FIG. 3, the example confidence score calculation 300 includes acontent score 302 and a metadata score 304 that are used to calculatedan expertise score as indicated by arrows 308 and 310, respectively.Content score 302 can be based on content types 312, associations 314,and/or combinations 316 of different content types and associations asshown by arrows 318, 320, and 322, respectively. The metadata score 304,moreover, can be based on tags 324, skills 326, and job titles 328 asindicated by arrows 330, 332, and 334, respectively. The calculation ofthe confidence score 356 is based on a constrained content score 336 anda constrained metadata score 338. The constrained content score 336 canbe based on the content score 302 and an evidence diversity 340 asindicated by arrows 342 and 344, respectively. The constrained metadatascore is based on the metadata score 304 and the evidence diversity 340as indicated by arrows 346 and 348, respectively. A social score 350 isbased on a social graph 352 as indicated by an arrow 354. The socialgraph 352 is based on the expertise score 306 as indicated by arrow 355.The confidence score 356 is based on the constrained content score 336,the constrained metadata score 338 and the social score 350, asindicated by arrows 358, 360 and 362, respectively.

As shown in FIG. 3, a content score 302 can be calculated based on anumber of content documents. For example, content documents can includeuser-created comments, shared information or documents, crowd-sourceddocuments, and the like. In some examples, document types can includewikis, blogs, and forums, and the like. Moreover, each content documentcan have one or more associations 314 with a potential expert. Forexample, a potential expert can be an author, commenter, and/or a liker,of a content document, among other possible associations 314. In someexamples, combinations 316 of document types 312 and associations 314can be given a predetermined weight. For example, a potential expert canbe given a particular amount of content score points for commenting in aforum on a particular subject of expertise.

The metadata score 304 can likewise be calculated based on tags, skills,and job titles, among other types of metadata. In some examples, themetadata score 304 for a potential expert can be affected by the amountof associated metadata as well as the weight of the metadata. Forexample, job titles may receive more weight than tags. Eachcharacteristic that is relevant to an expertise of the search query cancontribute to an expert's metadata score.

The content score 302 and the metadata score 304 can be used tocalculate an expertise score 306 for a number of expert profiles. Insome examples, a social graph 352 can be created from a set of potentialexperts based on the expertise score 306 of each potential expert. Forexample, a predetermined number of potential experts, such as the first1000 potential experts, of a particular expertise with higher expertisescores 306 can be used to create the social graph 352. The social graph352 can be used to calculate a social score 350 based on the number ofsocial connections a selected expert has with other selected experts inthe social graph 352. For example, the social score 350 can becalculated according to the detailed example given in FIG. 4 below.

In some embodiments, the content score 302 can be combined with anevidence diversity score 340 to produce a constrained content score 336.An evidence diversity score 340 is a number or rating that can indicatethe number of different types of evidence or associations to thisevidence that a confidence score 356 represents. In some examples, thediversity constraint can be based on threshold numbers of content typesand content associations, or combinations. For example, a threshold ofthree different content types and four different combinations of contenttypes and associations can be used for a high evidence diversity score340.

In some examples, the evidence diversity score 340 can be used toconstrain scores based on other dimensions. For example, the evidencediversity score 340 can be used to constrain the content score 302 andthe metadata score 304 based on the number sources each scorerepresents. A high score with a low evidence diversity score 340 may belimited to a low score corresponding to the evidence diversity score340. Likewise, the metadata score 304 can be combined with the evidencediversity score 340 to produce a constrained metadata score 338. Theconfidence score 356 can then be calculated from the constrained contentscore 336, the constrained metadata score 338 and the social score 350.In some examples, the confidence score 356 can be given a ranking oflow, medium or high confidence. For example, a potential expert with ahigh constrained content score 336, a high constrained metadata score338 and a high social score 350 can be given a high ranking for theconfidence score 356.

It is to be understood that the block diagram of FIG. 3 is not intendedto indicate that the system 300 is to include all of the componentsshown in FIG. 3. Rather, the confidence score 300 can include fewer oradditional components not illustrated in FIG. 3 (e.g., additionalcontent documents, or additional metadata, additional dimensions, etc.).

FIG. 4 is a block diagram of an example social score calculationaccording to embodiments described herein. The example social graph ofFIG. 4 is generally referred to by the reference number 400.

In FIG. 4, the example social score calculation 400 includes a selectedexpert 402 for which a social score 400 is to be calculated and threeother selected experts 404, 406, and 408, that are associated withselected expert 402 as indicated by arrows 410, 412 and 414,respectively. Selected experts 402, 404 and 406 are associated with oneor more documents in document store 416 as indicated by arrows 418, 420,and 422, respectively. In some examples, selected experts 402, 404, 406,and 408 can also be potential experts.

As shown in FIG. 4, the selected expert 402 receives points towards asocial score for each selected expert 404, 406, 408 of a particular areaof expertise. For example, the selected experts 402, 404, 406, and 408may be among the top 1000 practicing individuals in a particularexpertise as demonstrated by their expertise scores. In some examples,the selected experts 402, 404, 406, 408 may have scored a high expertisescore with regard to a particular expertise. For example, the selectedexperts 402, 404, 406, 408 may be among a predetermined number ofselected experts that were given a higher expertise score with regard toan expertise such as Java programming, among others. The selectedexperts 402, 404 and 406 are each associated with one or more Javadocuments that can serve as content evidence of Java expertise. Forexample, the documents can include blogs, crowd sourced documents, andstatus updates, among others. In some examples, the selected expert 402can receive a predetermined number of social points for being connectedto selected experts 404, 406, and 408. For example, selected expert 402may be a friend of selected expert 404 that also is a Java wikico-reader. In some examples, the selected expert 402 can receive apredetermined number of points for having a Java friend, and anotherpredetermined number of points because the Java friend is also a Javawiki co-reader. For example, selected expert 406 may be a Java projectmanager, friend and Java wiki co-reader of selected expert 402. Theselected expert can receive a predetermined number of points for beingconnected to a Java manager, another predetermined number points forbeing connected to a Java friend, and another predetermined number ofpoints for being connected to a Java co-reader. In some examples, thenumber of social score points received for each connection can beconfigurable in a configuration file.

In some examples, a selected expert 408 may not be associated with anydocuments of the document store 416. For example, the selected expert408 may be a friend of selected expert 402 with a metadata scoreindicating an association with the area of expertise. In some examples,the selected expert 402 can also receive an extra predetermined numberof points for being connected with selected expert 408.

It is to be understood that the block diagram of FIG. 4 is not intendedto indicate that the social graph 400 is to include all of thecomponents shown in FIG. 4. Rather, the system 200 can include fewer oradditional components not illustrated in FIG. 4 (e.g., additionalselected experts, or additional social relationships, etc.).

Referring now to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 502 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 504A, desktop computer 504B, laptop computer504C, and/or automobile computer system 504N may communicate. Nodes 502may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 504A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 502 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 600 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 602 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients. In oneexample, management layer 604 may provide the functions described below.Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 606 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and expertise searching.

FIG. 7 is a process flow diagram of an example method that can calculateconfidence scores. The method 700 can be implemented with any suitablecomputing device, such as the computing device 100 of FIG. 1 and isdescribed with reference to the system 200 of FIG. 2.

At block 702, the search engine 204 receives queries from client devices202. In some embodiments, the queries can include particular areas ofexpertise. For example, the area of expertise can be related to anysuitable technical area, such as Platform as a Service (PAAS) expertise,among others, or any other non-technical area of expertise.

At block 704, the search engine 204 executes a document-oriented searchbased on the query 226 in an index 208 of content documents 212, eachcontent document 212 associated with at least one potential expert 218,to output a set of document results. For example, the search engine 204can search through a search index 208 for content documents 212 relatingto the particular expertise. The search can be performed using searchengine configuration file 206.

At block 706, the search engine analyzes the document results to producea list of potential experts. For example, the search engine 204 cantraverse over the content documents 212 returned by the search of block704 and collect the names of potential experts associated with thecontent documents 212. Each potential expert receives a content scorebased on associations with one or more documents. For example, thecontent score can be based on the number of associations, the relevanceof each document to the expertise, and the nature of the association, orany combination thereof. In some examples, the content documents 212 mayalso have metadata. The metadata can be profile related information suchas tags, skills, and job title, etc. In some examples, each userreceives a metadata score based on the number, relevance, and nature ofthe metadata as the metadata relates to the particular expertise.

At block 708, the search engine can calculate an expertise score foreach potential expert 218 based on a calculated content score andmetadata score for each potential expert and sort the potential expertsby the expertise score. For example, the content score and metadatascore can be summed together to produce an expertise score. In someexamples, the expertise score can include three confidence levels basedon two thresholds. For example, the confidence levels can be high,medium, and low, the medium and high confidence levels each having athreshold. In some examples, if the expertise score exceeds a threshold,then the corresponding confidence level is met. In some examples, thethreshold values are configurable and reflect an amount and relevance ofevidence associated with a particular potential expert.

At block 710, the search engine 204 selects a predetermined number ofselected experts with higher expertise scores from the list of potentialexperts. In some examples, the predetermined number of selected expertsis configurable via a configuration file 206. Additional dimensions canthen be added to the confidence scores for each selected expert. Forexample, calculation of the confidence score may include combining themetadata scores and content scores with evidence diversity scores toproduce constrained content scores and constrained metadata scores, andcombining the constrained scores with a social score based on socialgraph 210 connections 220 to produce a confidence score as discussed ingreater detail below.

At block 712, the search engine 204 calculates an evidence diversityscore for each selected expert or potential expert. In some examples,the evidence diversity score is based on the quality and types of thecontent documents. Quality and variety of content documents can bedetermined using the configuration file 206. The configuration file 206can include types of content documents to be treated as different typesand different types of associations, and combinations thereof withcorresponding weights for scoring. For example, being tagged as anexpert and writing blog posts can produce a higher diversity score. Bycontrast, tagging other people as experts or posting on a wall mayproduce a lower diversity score. In some examples, the diversity scorecan be used as an additional constraint for medium and high confidencescore ratings. A threshold level of diversity score can be included in adiversity-constrained content score and a diversity-constrained metadatascore. For example, a diversity-constrained content score with a highcontent score but a low diversity score may result in a lowdiversity-constrained content score.

At block 714, the search engine 204 calculates a social score for eachselected expert or potential expert. The social score can be calculatedfrom a social graph 210 based on the number of connected experts 218 toa particular expert 218. In some examples, the social graph 210 iscomposed of selected experts based on higher expertise score. In someexamples, the social graph is composed of all potential experts. In someexamples, a social graph 210 is computed in the context of the query226. For example, an edge in the social graph 210 can connect twoexperts 218 that share an association with the same content document ifthe content document is relevant to the query 226. In some examples, theedges of the social graph 210 can be created during the initialtraversing over content documents returned for the query 226. In someexamples, context-free relations can also be included in the socialgraph 210. Context-free relations, as used herein, refer to socialrelations that are unrelated to the context of a query or content.Context-free relations can include, for example, a manager-employeerelationship or a friendship. In some examples, context-free relationscan also be included in the social graph 210 and included in the socialscore. In some examples, the social score is also divided intothresholds. For example, the social score can be split into threelevels, high, medium, and low, based upon two threshold values.

At block 716, the search engine 204 calculates a confidence score forthe selected experts based on at least one of the diversity-constrainedcontent score, the diversity-constrained metadata score, and the socialscore for each selected expert or potential expert. In some examples,the confidence score is calculated by combining thediversity-constrained content score, the diversity-constrained metadatascore and the social score following a configurable set of rules. Theconfigurable set of rules can include the amount of points received foreach confidence per dimension. For example, a selected expert 218 canreceive a point for having a low confidence level, two points for havinga medium confidence level, and three points for having a high confidencelevel for any of the three scores. The diversity-constrained contentscore, the diversity-constrained metadata score and the social score canbe summed together to produce a confidence score. Thus, the total pointsmay range, in some examples, from three to nine. If the selected expertor potential expert 218 receives eight or more total points, then theexpert 218 receives a high confidence score. If the selected expert orpotential expert 218 receives five to seven points, then the expertreceives a medium confidence score. If the selected expert or potentialexpert 218 receives four points or less, then the expert 218 receives alow confidence score. In some examples, the weights and thresholds forscoring can be configured in a search engine configuration file 206.

At block 718, the client device 202 displays a list of selected expertsor potential experts with associated confidence scores. In someexamples, the search engine 204 can return a list of results 228, suchas a list of experts, expertise scores, and evidence documents inaddition to the confidence scores associated with each expertise score.

The process flow diagram of FIG. 7 is not intended to indicate that theoperations of the method 700 are to be executed in any particular order,or that all of the operations of the method 700 are to be included inevery case. For example, block 710 may be skipped and blocks 712-718 maybe applied to all the potential experts rather than a subset of selectedexperts. Furthermore, the confidence score may be calculated using anyone or combination of the diversity-modified content score, thediversity-modified metadata score, and the social score. Additionally,the method 700 can include any suitable number of additional operations.

The present techniques may be a system, a method or computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentinvention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present techniques may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present techniques.

Aspects of the present techniques are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thetechniques. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Referring now to FIG. 8, a block diagram is depicted of an exampletangible, non-transitory computer-readable medium 800 that can calculateconfidence scores. The tangible, non-transitory, computer-readablemedium 800 may be accessed by a processor 802 over a computerinterconnect 804. Furthermore, the tangible, non-transitory,computer-readable medium 800 may include code to direct the processor802 to perform the operations of the current method.

The various software components discussed herein may be stored on thetangible, non-transitory, computer-readable medium 800, as indicated inFIG. 8. For example, a search engine module 806 includes code to executea document-oriented search based on a query in an index of documentseach associated with at least one potential expert, to output a set ofdocument results. The search engine module 806 also includes code toanalyze the document results to produce a list of potential experts. Aconfidence module 808 includes code to calculate an expertise score foreach potential expert based on a calculated content score and metadatascore for each potential expert and sorting the potential experts by theexpertise score. The search engine module 806 includes code to select apredetermined number of selected experts with higher expertise scoresfrom the list of potential experts and return a list of selectedexperts. The confidence module 808 includes code to calculate anevidence diversity score for each selected expert. A social graph module810 includes code to generate a social graph and calculate a socialscore from the social graph. In some examples, the social graph module810 can generate the social graph based on the list of selected experts.The confidence module 808 further includes code to calculate aconfidence score for the selected experts based on adiversity-constrained content score, a diversity-constrained metadatascore, and/or a social score. The search engine module 806 includes codeto filter the selected experts or the potential experts by theconfidence score.

In some examples, the query can include an expertise, the confidencescore to indicate a relative level of the expertise for each selectedexpert. In some examples, the confidence module 808 can calculate thecontent score based on different content types including wikis, blogs,and status updates, and different associations such as author,commenter, and liker, stored in a data repository. In some examples, theconfidence module 808 can calculate the metadata score from tags,skills, and job titles of the potential experts stored in a datarepository. In some examples, the evidence diversity score includes apredetermined threshold number of different content types, contentassociations, metadata types, or any combinations thereof. In someexamples, the social score is to be based on a number of connectionsbetween a selected expert and other selected experts. In some examples,the social score is to be based on a number of connections between apotential expert and other potential experts. For example, the socialgraph module 810 can use a social graph to analyze connections betweenselected experts or potential experts.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present techniques. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. It is to be understood that any number ofadditional software components not shown in FIG. 8 may be includedwithin the tangible, non-transitory, computer-readable medium 800,depending on the specific application.

The descriptions of the various embodiments of the present techniqueshave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method, comprising: executing, via a processor,a document-oriented search based on a query in an index of documents togenerate a set of document results, each document in the set of documentresults is associated with at least one potential expert; analyzing, viathe processor, the document results to produce a list of potentialexperts; calculating, via the processor, an expertise score for eachpotential expert based on a calculated content score and metadata scorefor each potential expert; calculating, via the processor, an evidencediversity score for each potential expert; calculating, via theprocessor, a confidence score for each potential expert based on adiversity-constrained content score and a diversity-constrained metadatascore for each potential expert, wherein: the diversity-constrainedcontent score is calculated using the evidence diversity score,comprising a predetermined threshold number of different activitiesassociated with the potential expert, and the content score for thepotential expert; the diversity-constrained metadata score is calculatedusing the evidence diversity score and the metadata score for thepotential expert; the content score is calculated based on a number ofdifferent content document types and associations associated with thepotential expert, the content document types and associations aregathered by parsing websites and stored in a data repository; themetadata score is calculated based on profile-related informationassociated with the potential expert; and the confidence score isfurther calculated based on a social score, wherein the processor isconfigured to generate a representation of connections between thepotential experts, the social score for each potential expert iscalculated using the representation of connections and based on a numberof connections to other potential experts; and sending a list ofpotential experts whose confidence scores are above a confidence scorethreshold to a client device.
 2. The method of claim 1, furthercomprising: selecting, via the processor, a predetermined number ofpotential experts whose expertise scores meeting a threshold expertisescore from the list of potential experts; calculating, via theprocessor, the evidence diversity score for each selected expert; andcalculating, via the processor, a confidence score for each selectedexpert using the evidence diversity score for each selected expert. 3.The method of claim 2, further comprising sorting the list of potentialexperts by the expertise scores, wherein the predetermined number ofselected experts being selected from the sorted list of potentialexperts.
 4. The method of claim 1, further comprising calculating thediversity-constrained content score comprising constraining the contentscore based on the evidence diversity score and calculating thediversity-constrained metadata score comprising constraining themetadata score based on the evidence diversity score.
 5. The method ofclaim 1, wherein the confidence scores are calculated based onpreconfigured thresholds.
 6. The method of claim 1, wherein the queryindicates an expertise, and the confidence score is used to indicate alevel of certainty in the expertise for each potential expert.
 7. Themethod of claim 1, wherein the list of potential experts is filteredaccording to the confidence scores and sorted by the expertise scores.